This paper deals with an application of stability approach. In such a case, it is very important to be able to estimate the maximum tracking error induced by uncertainties and/or bounded perturbations. In this paper, we present a new approach which enables the estimation by overvaluation of this error between the evolution of Lur'e and Postnikov systems and of its model. It is based on the use of the aggregation techniques using vector norms and comparison systems.

This paper presents an adaptation of harmony search algorithm to solve unbound container storage location. Our aim is to reduce the time of unloading all the containers when they are delivered to their customers. In fact, we minimize the wait time of customer trucks and the transfer time of yard crane. This problem is studied considering multiple container type (dry, tank and refrigerated). The performance of the proposed approach is verified comparing to the results generated by current practice at ports.

This paper deals with a new approach for complex systems modeling and control based on neural and fuzzy clustering algorithms. It aims to derive a base of local models describing the system in the whole operating domain. The implementation of this approach requires three main steps: 1) determination of the structure of the model-base, the number of models are found out by using Rival Penalized Competitive Learning (RPCL), and the operating clusters are selected referring to the fuzzy K-means algorithm, 2) parametric model identification using the clustering results 3) determination of the global system control parameters obtained by a fusion of local control parameters. The case of a second order nonlinear system is studied to illustrate the efficiency of the proposed approach.

The real-time regulation of urban collective transport traffic is a very delicate problem, particularly in case of appearance of simultaneous disturbances (vehicle's breakdown, strike, demonstration, etc.). Indeed, a regulator (a decision maker) has to carry out difficult tasks that are often inaccessible at the human scale and involve the assistance of a decision support system (DSS). In this paper, we present the main part of this DSS, i.e., the generator and the evaluator of the decision strategies for disrupted transport network regulation. The proposed module is based on a hybrid approach using a fuzzy evaluation method and evolutionary algorithms. It treats the online regulation problem as an optimization one and provides the regulator with evaluated and classified effective decisions by taking into account his/her preferences.

An indirect iterative learning algorithm has shown to be able to update the parameters of an acceleration feedback controller for flexible manipulators. The fine estimation of the masses of a chain of mass-spring-dampers units allows the simultaneous tuning of both the feedback controller and the trajectory generation. This algorithm has been validated on an industrial robot arm.

In this paper, we propose a new hierarchical method for the flexible job-shop scheduling problem (FJSP). This approach is mainly adapted to a job-shop problem (JSP) with high flexibility and is based on the decomposition of the problem in an assignment subproblem and a sequencing subproblem. For the first subproblem, we propose two methods: the first one is based successively on a heuristic approach and a local search; the second one, however, is based on a branch-and-bound algorithm. The quality of the assignment is evaluated by a lower bound. For the second subproblem we apply a hybrid genetic algorithm to deal with the sequencing problem. Computational tests are finally presented.

In the single-vehicle pickup and delivery problem with time windows (1-PDPTW), a vehicle must serve a collection of transportation requests by taking loads from providers to customers satisfying precedence, capacity and time constraints. In this paper, we present a brief scientific literature review on the 1-PDPTW and we propose an evolutionary approach, based on lower bounds, to solve this problem. Our evolutionary approach uses an evolutionary algorithm, with special genetic operators to minimize computing time and Pareto dominance method to provide a set of satisfying and feasible solutions to the 1-PDPTW, minimizing the compromise between total travel distance and total tardiness time

The framework of the results presented here is the modelling approach with multiple models, for example the one used in a polytopic Takagi-Sugeno-like modelling and control of bounded non linear and uncertain systems where the base of models is composed of the vertices (or extreme) models. This paper proposes to sort out the problem of updating the state of each model encountered when estimating online the validities of each model in a multiple model approach. Moreover, the internal observations of each local-model state can be combined with these validities to control the system with a multi-control strategy, as the validity estimation is enhanced and more accurate. In the Takagi-Sugeno (TS) context, the online validities can be substituted to the contribution functions, giving better practical stability results.

This paper deals with the real-time regulation of traffic within a disrupted transportation system. We outline the necessity of a decision support system that detects, analyzes, and resolves the unpredicted disturbances. Due to the distributed aspects of transportation systems, we present a multi-agent approach for the regulation process. Moreover, this approach also includes an evolutionary algorithm that is based on an original genetic coding representing the decisions on a set of vehicles and stops affected by the disturbance. This set constitutes, in fact, the space-time horizon of the regulation process. The evolutionary algorithm then treats the regulation problem as an optimization and provides the regulator with relevant decisions that can result in a partial reconfiguration of the network.

This paper considers the problem of locating the GDF (Gaz de France) bases of the French gas board in a geographical region in order to supervise and to maintain a set of stations and gas pipe lines with specific security and efficiency levels where some GDF base locations can be fixed a priori. The location problem reduces to a classical location allocation problem with additional side and security constraints. To solve the location problem we propose a genetic algorithm where solutions are evaluated based on their cost, their security and their efficiency levels. The genetic algorithm is embedded within a decision support tool to generate acceptable solutions to the decision maker. The decision support tool allows the decision maker to be involved in the decision process by setting the parameters of the location problem and the parameters of the genetic algorithm such as the initial population and the stopping criterion.

In our previous work (2002), we have proposed a Pareto-optimality approach for solving multiobjective optimization problems (MOPs) based on the hybridization of fuzzy logic and evolutionary algorithms (EAs). Such an approach makes it possible to construct a set of satisfactory solutions in order to provide flexibility to the decision-maker. In this work, we aim to enhance the suggested approach and propose a new variant of such a hybridization. Thereafter, we show how a uniform design can be used for finding a set of Pareto optimality solutions uniformly scattered. We briefly describe the Pareto-optimality concepts used for solving MOPs and those especially applied in EAs. Then, the mathematical formulation of FJSP is presented. The proposed hybrid approach is described. We illustrate the suggested approach by applying it to solving FJSP and highlights some practical aspects of the application of such an approach for solving hard combinatorial problems. Finally, we conclude with some future research directions.

The increasing complexity of industrial processes and the higher performance requirements, make the multimodel approach necessary. This approach is often confronted to the determination of the useful models' base problem. An approach for a systematic determination of a models' base for the representation of uncertain linear systems is suggested in this article. The application of this approach requires two main steps. We are firstly interested in the classification of the data by using Kohonen self-adapting artificial neural network and in the determination of the necessary number of models. Secondly, we exploit the obtained data relative to the clusters for the structural and parametric identification of different base-models. An experimental validation of the suggested modelling approach is carried out on an olive oil esterification reactor. The obtained results are satisfactory and show a very good precision relatively to the case in which the classical modelling, based on a unique model, is adopted.

Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.

International audience

The design of stable fuzzy controllers can be achieved by determining a series of embedded sets for which the attractor of any set is included into the next set of the series. The framework of the comparison principle, along with overvaluing systems and vector norms, allows the computation of the attractors and to handle the robustness with respect to uncertainties. Considering the inverted pendulum control problem, this procedure is applied to the parameter tuning of a fuzzy controller with singleton defuzzification, whose regionwise structure leads to a rational partition of the state space.

The most important disadvantage of urban bus networks (UBN) is their close dependence on diverse random phenomena that can alter the initial preestablished schedules of the buses. In fact, any changes occurring in the global urban traffic, in the demand, in the equipment or in the stuff, may certainly cause disturbances within the network traffic. Moreover, these disturbances affect the waiting time of the customers at the stops, the transit operations and also the duration of the different trips on the buses. This article deals with the traffic regulation in an UBN, especially with the transit operations. After introducing the considered disturbances, we state the different criteria that have to be optimized. The regulation approach by evolutionary algorithms is then exposed and some simulation results are presented.

Traditionally, assignment and scheduling decisions are separately made at different levels of the production management framework. The combining of these decisions presents additional complexity and new problems. We present two approaches to solve the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization : it makes it possible to solve the problem of resources allocation and build an ideal assignments model (assignments scheme). The second one is an evolutionary approach controlled by the assignments model (generated by the first approach). In this approach, we apply advanced genetic manipulations in order to enhanced solutions quality. We Also explain some of the practical and theoretical considerations to the construction of a more robust encoding that will allow us to consider the sequencing and the assignment problems jointly.

The use of the multimodel approach in the modeling, analysis and control of nonlinear complex and/or ill-defined systems was advocated by many researchers. This approach supposes the definition of a set of local models valid in a given region or domain. Different strategies exist in the literature and are generally based on a portioning of the nonlinear system's full range of operation into multiple smaller operating regimes each of which is associated with a locally valid model or controller. The global control may be a fusion between the partials laws. The purpose of the paper is to pay attention to interesting aspects of the multimodel approach thus revealing some answers to problems not yet resolved for multivariable systems.

This paper outlines a methodology to study the stability of Takagi-Sugeno's fuzzy models using a systems of linear matrix inequalities (LMIs). The Takagi-Sugeno's model is first introduced. A stability analysis is then performed using a quadratic Lyapunov candidate function. This paper proposes a relaxation of Tanaka's stability condition: the LMIs to be solved are not Lyapunov equations for each rule matrix, but convex combinations of them. The coefficients of these sums depend on the membership functions. A generalization to the case of stabilization via parallel distributed compensation regulators is also proposed, with an application to a model of inverted pendulum.

The purpose of this paper is to improve the robustness of the multimodel control of discrete systems by using observers. The new strategy of the multimodel control is based on the use of a dynamic library of generic models and observers. The main idea of this paper is to re-initialize the state of each model by the corresponding state estimated from the process when the error between them exceeds a certain value. A good choice of the observer besides a fusion applied on the coefficients of each controller allows the multimodel control strategy to reach the desired objective for the global control of the system. The desired performance is reached by the use of this new strategy of multimodel control. Then, the breakthrough aspect of this work is the introduction of observers in the multimodel control as a technique to improve the validity of the generic models. The global control obtained by this new strategy of control is presented. Computer simulation results show that a design of robust multimodel control based on a good selection of the models plus re-initialization steps based on observers is feasible and provides good performance.

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Most studies dealing with mines detection emphasise sensors and processing linked to them. However, most people agree that one sensor cannot be the solution to the detection problems: a system including several sensors seems to be the solution. It implies that a data fusion function must be realized in this system. We propose in this paper the derivation of a fusion operator based on fuzzy logic. We also expose a sensitivity study of this operator with respect to several parameters as the depth or the geometry of the estimated shape of a buried detected object.

This paper explain why and how to implement the double loops service in the automatic subway of Lille, France. This double loops service is characterized by the presence of more possible circuit for the train. The system permits one to serve some portions of the lines, which have some high density of population. We explain how to solve the different problems caused by traffic perturbation.

Possibility theory aims at representing and handling uncertain information. An important property of this theory is the ability to merge different data sources in order to increase the quality of the information. Different fusion rules have been defined in the literature, each with its own advantages and drawbacks. However, these rules usually cannot deal rigorously with contradictory data. Therefore, this paper proposes a new approach, based on a fusion rule using a vector expressing the reliability of the data sources. Comparisons are made with classical fusion rules. An algorithm assessing the indexes of reliability of the data used by the new role is given, and an index of the quality of the result is proposed. These three tools define a new method dealing with the reliability of the data in the fusion field, and enable a clear distinction between the data and their quality.

The use of a nonlinear context dependent operator based on possibility theory is studied in order to provide a generic fusion mechanism in different parts of the control of a robot in an uncertain, ill-defined environment. Its main interest consists in the definition of a reliability degree for each partial result which may allow a more pertinent adaptive mechanism.